Inferring disease architecture and predictive ability with LDpred2-auto

• Published in: American journal of human genetics 2023-12, Vol.110 (12), p.2042-2055
• Main Authors: Privé, Florian, Albiñana, Clara, Arbel, Julyan, Pasaniuc, Bogdan, Vilhjálmsson, Bjarni J.
• Format: Article
• Language: English
• Subjects: Bayes Theorem; Genome-Wide Association Study - methods; Humans; inference; LDpred2; Multifactorial Inheritance - genetics; Polymorphism, Single Nucleotide - genetics
• ISSN: 0002-9297; 1537-6605; 1537-6605
• DOI: 10.1016/j.ajhg.2023.10.010

LDpred2 is a widely used Bayesian method for building polygenic scores (PGSs). LDpred2-auto can infer the two parameters from the LDpred model, the SNP heritability h2 and polygenicity p, so that it does not require an additional validation dataset to choose best-performing parameters. The main aim of this paper is to properly validate the use of LDpred2-auto for inferring multiple genetic parameters. Here, we present a new version of LDpred2-auto that adds an optional third parameter α to its model, for modeling negative selection. We then validate the inference of these three parameters (or two, when using the previous model). We also show that LDpred2-auto provides per-variant probabilities of being causal that are well calibrated and can therefore be used for fine-mapping purposes. We also introduce a formula to infer the out-of-sample predictive performance r2 of the resulting PGS directly from the Gibbs sampler of LDpred2-auto. Finally, we extend the set of HapMap3 variants recommended to use with LDpred2 with 37% more variants to improve the coverage of this set, and we show that this new set of variants captures 12% more heritability and provides 6% more predictive performance, on average, in UK Biobank analyses. This study extends the utility of LDpred2-auto, a widely used method for deriving predictive polygenic scores. It validates the use of the latest version of LDpred2-auto for inferring key genetic parameters that inform us about the genetic architecture of traits, for identifying causal genetic variants, and for estimating predictive performance.